# Polymorphic cubes

In programming languages, polymorphism usually means the use of a single symbol
to represent multiple different types. It can be quite common for a database and
application to be designed in such a way that leverages a single database table
for entities of different types that share common traits.

For example, you are working on an online education platform, where teachers
assign lessons to students. The database can contain only two tables: one for
`users` and another one for `lessons`. The `users` table can contain a `type`
column, with possible values `teacher` or `student`. Here is how it could look:

| **id** | **type** | **name**       | **school**         |
| ------ | -------- | -------------- | ------------------ |
| 1      | student  | Carl Anderson  | Balboa High School |
| 2      | student  | Luke Skywalker | Balboa High School |
| 31     | teacher  | John Doe       | Balboa High School |

Lessons are assigned by teachers and completed by students. The `lessons` table
has both `teacher_id` and `student_id`, which are actually references to the
`user id`. The `lessons` table can look like this:

| **id** | **teacher_id** | **student_id** | **name**                                      |
| ------ | -------------- | -------------- | --------------------------------------------- |
| 100    | 31             | 1              | Multiplication and the meaning of the Factors |
| 101    | 31             | 2              | Division as an Unknown Factor Problem         |

The best way to design such a data model is by using what we call **Polymorphic
Cubes**. It relies on the [`extends`][ref-schema-ref-cubes-extends] feature and
prevents you from duplicating code, while preserving the correct domain logic.
Learn more about using [`extends` here][ref-schema-advanced-extend].

The first step is to create a `user` cube, which will act as a base cube for our
`teachers` and `students` cubes and will contain all common measures and
dimensions:

<CodeTabs>

```yaml
cubes:
  - name: users
    sql: SELECT * FROM users

    measures:
      - name: count
        type: count

    dimensions:
      - name: name
        sql: name
        type: string

      - name: school
        sql: school
        type: string
```

```javascript
cube(`users`, {
  sql: `SELECT * FROM users`,

  measures: {
    count: {
      type: `count`
    }
  },

  dimensions: {
    name: {
      sql: `name`,
      type: `string`
    },

    school: {
      sql: `school`,
      type: `string`
    }
  }
})
```

</CodeTabs>

Then you can derive the `teachers` and `students` cubes from `users`:

<CodeTabs>

```javascript
cube(`teachers`, {
  extends: users,
  sql: `
    SELECT *
    FROM ${users.sql()}
    WHERE type = 'teacher'
  `
})

cube(`students`, {
  extends: users,
  sql: `
    SELECT *
    FROM ${users.sql()}
    WHERE type = 'student'
  `
})
```

```yaml
cubes:
  - name: teachers
    extends: users
    sql: |
      SELECT *
      FROM {users.sql()}
      WHERE type = 'teacher'

  - name: students
    extends: users
    sql: |
      SELECT *
      FROM {users.sql()}
      WHERE type = 'student'
```

</CodeTabs>

Once we have those cubes, we can define correct joins from the `lessons` cube:

<CodeTabs>

```yaml
cubes:
  - name: lessons
    sql_table: lessons

    joins:
      - name: students
        relationship: many_to_one
        sql: "{CUBE}.student_id = {students.id}"

      - name: teachers
        relationship: many_to_one
        sql: "{CUBE}.teacher_id = {teachers.id}"
```

```javascript
cube(`lessons`, {
  sql_table: `lessons`,

  joins: {
    students: {
      relationship: `many_to_one`,
      sql: `${CUBE}.student_id = ${students.id}`
    },

    teachers: {
      relationship: `many_to_one`,
      sql: `${CUBE}.teacher_id = ${teachers.id}`
    }
  }
})
```

</CodeTabs>

[ref-schema-advanced-extend]:
  /product/data-modeling/concepts/code-reusability-extending-cubes
[ref-schema-ref-cubes-extends]: /product/data-modeling/reference/cube#extends

